Methods for Traffc Data Classifcation with regard to Potential Safety Hazards
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
SYSTEM IDENTIFICATION: learning models for decision and control, Sysid 2021
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Abstract: Trafc data are a key element for setting up scenarios for Advanced Driver Assistant
Systems (ADAS) safety and performance testing. Testing will thus reflect in some way the data
used. However, there is no clear understanding in which way and how to choose the data so that
the evaluation results are reliable and comprehensive. Therefore, the important scenarios in a
traffic data set in view of safety analysis have to be determined. The paper presents a method
with which traffic situations from a given data set are classified into different safety classes
according to easily measurable features. It is shown that taking the Time To Collision (TTC)
as a measure of safety and a linear Support Vector Machine (SVM) as a classifer, 64.7% of
trafc situations of a validation data set were classifed to the correct safety class considering
only three measurable features. Thus, traffic situations from a data set can be classifed fast into
diferent safety categories, providing information to the ADAS tester if the developed device
has been tested in a safe or unsafe environment.
Forschungs-